EP2544133A1 - Réservations de restaurant selon les offres - Google Patents

Réservations de restaurant selon les offres Download PDF

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Publication number
EP2544133A1
EP2544133A1 EP12175433A EP12175433A EP2544133A1 EP 2544133 A1 EP2544133 A1 EP 2544133A1 EP 12175433 A EP12175433 A EP 12175433A EP 12175433 A EP12175433 A EP 12175433A EP 2544133 A1 EP2544133 A1 EP 2544133A1
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EP
European Patent Office
Prior art keywords
offers
offer
restaurant
availability
available
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP12175433A
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German (de)
English (en)
Inventor
Charlie Mccullough
Elaine Teoh
Edgard Valdivia
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OpenTable Inc
Original Assignee
OpenTable Inc
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Filing date
Publication date
Application filed by OpenTable Inc filed Critical OpenTable Inc
Publication of EP2544133A1 publication Critical patent/EP2544133A1/fr
Withdrawn legal-status Critical Current

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/02—Reservations, e.g. for tickets, services or events
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising

Definitions

  • Restaurants typically offer promotions that generally improve the desirability for a customer to patronize a given business. For example, happy hour promotions or coupons for a free entrée. However, the restaurant has limited control over the number of customers that arrive or when they arrive in response to these promotions.
  • Figure 1 is a block diagram illustrating an embodiment of a system for offer based restaurant reservations.
  • Figure 2 is a block diagram illustrating an embodiment of a service provider system.
  • Figure 3A is a flow diagram illustrating an embodiment of a process for offer based restaurant reservations.
  • Figure 3B is a flow diagram illustrating an embodiment of a flow for offer based restaurant reservations.
  • Figure 4 is a diagram illustrating an embodiment of a user interface display.
  • Figure 5 is a diagram illustrating an embodiment of a user interface display.
  • the invention can be implemented in numerous ways, including as a process; an apparatus; a system; a composition of matter; a computer program product embodied on a computer readable storage medium; and/or a processor, such as a processor configured to execute instructions stored on and/or provided by a memory coupled to the processor.
  • these implementations, or any other form that the invention may take, may be referred to as techniques.
  • the order of the steps of disclosed processes may be altered within the scope of the invention.
  • a component such as a processor or a memory described as being configured to perform a task may be implemented as a general component that is temporarily configured to perform the task at a given time or a specific component that is manufactured to perform the task.
  • the term 'processor' refers to one or more devices, circuits, and/or processing cores configured to process data, such as computer program instructions.
  • a system for offer based restaurant reservations comprises a processor and a memory.
  • the processor is configured to: receive a request for a reservation including a set of attributes such as a date or date range, a time range, a location, a cuisine, and a party size; determine a set of available reservations based on an actual table availability for the date, time, and the party size; determine one or more offers based on the date, time, and the party size; determine an overlapping subset between the set of available reservations and the one or more offers; and provide an indication of the overlapping subset.
  • the memory is coupled to the processor and is configured to provide the processor with instructions.
  • a system for offer based restaurant reservations comprises a processor and a memory.
  • the processor configured to generate a plurality of available tables using availability rules, filter the plurality of available tables to determine a set of pertinent available tables, generate a plurality of offers using offer rules, filter the plurality of offers to determine a set of pertinent offers, determine an overlapping subset between the set of pertinent available tables and the set of pertinent offers, and in the event that a trigger event occurs, provide an offer to a user from the overlapping subset.
  • the memory is coupled to the processor and is configured to provide the processor with instructions.
  • the process for determining a response to a search for reservations comprises determining availability of tables and availability of offers.
  • Availability of tables is determined by using availability rules to generate availability and then filtering the availability to find the pertinent availability.
  • Availability of offers is determined by using offer rules to generate all offers and then filtering the offers to find the pertinent offers.
  • the pertinent availability of tables and the pertinent offers are searched to see which restaurants/tables occurred in both sets (pertinent availability of tables and pertinent offers).
  • the restaurant/tables are subject to yield management/revenue optimization rules before presenting the result to a customer.
  • the system provides search results to a user such as a particular offer type. For example, "all 50% off deals in SF" are searched for in the system and presented to the user.
  • a restaurant is able to make offers that are specific to available tables at the restaurant at a specific time.
  • the restaurant is thereby able to control the number and arrival rate of the customers that receive promotional offers in order to target the promotions to fill their tables but not overfill the restaurant.
  • the system calculates all available possible combinations of available tables and offers promotions to reduce available tables.
  • the system dynamically removes combinations as tables are reserved.
  • the system automatically generates offers based on a rule set for available tables. For example, a restaurant offers 50% off for the first $20 of a check for tables available 2 days from today but only on Monday through Thursday between 5 PM and 3:1 PM.
  • the system makes offers that are dynamically updated based on table availability.
  • the offers are made considering customer arrival rate, available tables, restaurant capacity, revenue optimization, customer retention, party size, date, time of the day, day of the week, special events, theater days, holidays, percentage booked, total number of offers per day, total offers per shift, total accepted offers per day, total accepted offers per shift, up to a certain number of offers over a period, or any other appropriate consideration.
  • the offers are offered for a requested time in the event that the requested time is available.
  • restaurant table availability is based on 1) allocation models or allotment, where the restaurant indicates the number of tables, covers, or parties that are available for a shift or a rate of arrival, 2) slot model, where the restaurant indicates the party sizes and exact times that there are available, 3) inventory where the restaurant indicates the specific tables and times that are available and the system determines specific party sizes and times, or any other appropriate manner of determining table or reservation availability.
  • a reservation and an offer are only offered in combination and are rolled back if the reservation is not completed or canceled.
  • the offer is made as a paperless (e.g., offered via a network, on a cell phone, on a computer accessing a web service).
  • the offer is made as a discrete transaction that is made between a customer searching for a reservation and a service that provides reservations as a service. The offer and availability is tailored for a discrete transaction between a user and a service where the transaction can be accepted or if not accepted, withdrawn.
  • offers are based on any of: revenue, covers, parties,- all of these computed from current trend data and/or historical data with varying granularities (e.g., day, week, month, year, same day of prior week, same week of prior year, etc.), or any other appropriate basis for making offers.
  • granularities e.g., day, week, month, year, same day of prior week, same week of prior year, etc.
  • the system for making offers is accessed or integrated with a 3 rd party system via an application programming interface (API).
  • API application programming interface
  • the system is used with a discount service (e.g., a service that distributes a discount via email - for example, Groupon TM , a service that offers a discount via the web - for example, livingsocial, etc.), where an offer is coupled directly with a table available at a restaurant.
  • a discount service e.g., a service that distributes a discount via email - for example, Groupon TM , a service that offers a discount via the web - for example, livingsocial, etc.
  • Figure 1 is a block diagram illustrating an embodiment of a system for offer based restaurant reservations.
  • user 100 communicates via network 104 with web server(s) 106.
  • User 100 requests availability of a restaurant reservation.
  • Web server(s) 106 provide information regarding available restaurant reservation (e.g., search results, reviews, time availability, menus, seating chart, table layouts, special services, etc.) and/or available offers (e.g., based on party size, time, date, restaurant type, etc.).
  • Web server(s) 106 communicate directly or via network 104 with service provider system(s) 102.
  • Web server(s) 106 communicate reservation availability information, reservation confirmation information, offer availability, offer confirmation, menu information, seating chart and/or table layout information, etc.
  • a network coupled to the processor to a publicly accessible web site via the Internet provides functionality of one or more of the following: functionality to transmit availability of reservations and/or offers, functionality to transmit requests from the web site, functionality to transmit returned reservation and/or offer acceptance information, or any other appropriate functionality.
  • a graphical user interface associated with web server(s) 106 or service provider system(s) 102 is coupled to the processor for one or more of the following: to receive and return reservation requests, to receive and return offers, to provide reservation availability, to provide reservation confirmation information, to receive offer rules, to automatically generate offers, and/or for any other appropriate functionality.
  • the functions of specifying, configuring or changing offers and associated limits and triggering conditions are done through a separate system or by an equivalent of service provider system 102.
  • the offer setup and rule input is performed using a business to business application or at a web server or in other appropriate manner.
  • FIG. 2 is a block diagram illustrating an embodiment of a service provider system.
  • service provider system 200 of Figure 2 is used to implement one system of service provider system(s) 102 of Figure 1 .
  • service provider system 200 comprises touch screen 202, web server interface 206, database 208, and inventory manager 210.
  • Touch screen 202 is used to interface to service provider system 200.
  • a restaurant manager interfaces with service provider system 200 using touch screen 202 to navigate, enter commands and parameters, or any other appropriate interaction with the service provider system.
  • Web server interface 206 interfaces system provider system 200 and a web server.
  • Web server interface 206 is used to communicate reservation information, confirmation information, configuration information, menu information, or any other appropriate information.
  • Database 208 stores service provider information.
  • Service provider information includes reservation information, confirmation information, menu information, configuration information, etc.
  • Inventory manager 210 comprises configuration viewer 212, configuration initialize 214, combination & permutation engine 206, query handler 218, and rule engine 220. Inventory manager 210 enables more efficient use of restaurant inventory by offering availability of all possible combinations and/or permutations of restaurant reservations. Then upon receipt of a new reservation, the list of possible combination and/or permutations is updated to remove combinations and/or permutations that are eliminated by the new reservation. For example, combinations and/or permutations that is/are not possible because the new reservation conflicts with them, i.e., use the same table within the same time period or likely time period based on historical average time used for reservations of this party size or by this diner, etc. Configuration viewer 212 enables a service provider employee to view the restaurant configuration.
  • the configuration information includes table configuration for restaurant (e.g., location of table, combination possibilities, number of seats, etc.).
  • Configuration initializer 214 is used to initialize the configuration for the restaurant.
  • Combination & permutation engine 216 is used to determine the possible combinations and/or permutations of reservations available for a restaurant given the configuration of the restaurant and the existing reservations.
  • Query handler 218 is used to handle queries regarding table availability. For example, a query is made regarding the available reservations that include all possible permutations and combinations of available times and available table configurations for a given restaurant.
  • Rule engine 220 is used to determine a selection of a reservation/table/time slot based on a rule. Rule engine 220 is able to automatically select a reservation for a table/time slot.
  • Offer engine 222 is used to determine available offers for a restaurant given a date, a time, a table size, either based on specific offers set by a specified desired offer list or by an automated offer generation engine that determines offers based on a rule set that is dependent on factors (e.g., customer arrival rate, percentage booked, date, day of the week, restaurant capacity, revenue optimization, customer retention, resource optimization, time, time slot, day of the month, holiday, local event, days to reservation, etc.).
  • factors e.g., customer arrival rate, percentage booked, date, day of the week, restaurant capacity, revenue optimization, customer retention, resource optimization, time, time slot, day of the month, holiday, local event, days to reservation, etc.
  • the selection of the reservation is based on a rule enabling automatic selection.
  • the rule for selecting a reservation comprises maximizing table availability (e.g., a best fit followed by a selection of maximizing table availability), minimizing wait time, balancing load to wait staff, utilizing tables in a specific order (e.g., near windows first, back of the restaurant last, or any other appropriate rule.
  • the selection of the reservation is determined based at least in part on one or more of the following: maximizing revenue (e.g., maximizing revenue can cause more/longer wait times), maximizing customer satisfaction (e.g., maximizing customer satisfaction can underutilize the restaurant), a historical turn time (e.g., used in the prediction of a table availability), a received turn time (e.g., from a maitre d') a historical restaurant reservation information (e.g., used in the prediction of customer arrivals or customer flow), a historical customer search information (e.g., used in the prediction of customer demand and potential flow for tables in the restaurant; where historical can mean that the rule is based on the previous day, the same day of the previous week, month, or year, or any other meaningful earlier period such as a holiday period), a maximum arrival rate for customers at a restaurant (e.g., where the maximum arrival rate is based on an individual arrival rate, a party arrival rate, a large party arrival rate, etc. and a time segment for example
  • Figure 3A is a flow diagram illustrating an embodiment of a process for offer based restaurant reservations.
  • a request is received for a reservation including a date, a time, and a party size.
  • a set of available reservations is determined based on actual table restaurant availability.
  • the actual table availability is determined by calculating combinations and permutations for table availability of a restaurant. For example, restaurants have tables that can seat patrons. Tables can be combined or rearranged. This ability to combine or rearrange or separate tables means that there are multiple possible configurations and multiple possible available reservations. These can be made available and then in the event that some reservations are filled, a new availability is determined.
  • actual table availability is determined in real time, where the determination of availability uses a combination and/or permutation calculation based at least in part on up to date reservation information, usage information, configuration information, or any other appropriate information.
  • actual table availability is based on one or more of the following: a daily maximum cover, a number of seated covers, a diner's location, a diner's proximity to a restaurant, a diner's history of restaurant use, an arrival rate limit, a user behavior, a user geographic location, a user spending history, a visit history, a time spent on a page, a page viewing history, a dining history, a preferred cuisine, a dining location or any other appropriate criteria.
  • offer(s) is/are determined based on the date, the time, and the party size. Offer(s) are made in order to increase use of the restaurant's available tables. In some embodiments, the one or more offers is/are determined in real time. For example, the offers are determined based at least in part on a start date for an offer, a stop date for an offer, offer days (e.g., Thursdays, Monday-Wednesday, etc.), offer hours (e.g., 11:00 AM - 3:00 PM, 4:30 PM - 6:00 PM, etc.), daily maximum covers, a number of seated covers, a cap for number of offers, a number of offers, an amount of offer (e.g., dollars off, percentage off, percent meal discount, percent beverage discount, buy one get one free, complimentary item, free corkage, familia fixe meal, etc.), an arrival rate limit (e.g., so many per hour, per 15 minutes, etc.), etc.
  • a start date for an offer e.g., Thursdays, Monday-
  • offers of 10% off are setup between January 10, 2011 and January 20, 2011 for Tuesdays through Thursdays for the hours of 3:00 PM to 4:00 PM but with not more than 2 offers per each 15 minute period.
  • the one or more offers is based at least in part on a minimum party size and/or a maximum party size.
  • an overlap subset is determined between the set of available reservations and the offer(s).
  • the overlapping subset includes one or more reservations each with an associated offer for the date and the party size.
  • the overlapping subset includes one or more reservations each with an associated offer for times around the time.
  • the overlapping subset is provided.
  • offers are triggered based on user behavior - for example, a trigger event is triggered based on a time spent on page, a page viewing history, a dining history, preferred cuisines, dining locations, a diner's location, a diner's proximity to a restaurant, an availability of an offer, a diner's history of restaurant use, etc.; In some embodiments, offers are triggered based on a user geographic location; In some embodiments, offers are triggered based on revenue or resource optimization - for example, maximizing utilization of staff or maximizing utilization of an area of the restaurant; In some embodiments, offers are triggered by a diners' spending history or by a visit history at a particular restaurant (e.g., generating an offer to a previously frequent diner who hasn't been in lately).
  • offers are made based on prespecified conditions and/or prespecified rules. For examples, an offer is only available to diners who are located within 1 mile of the restaurant at the time that they book the reservation; an offer is generated for 'section 1' of the restaurant until it is 90% utilized; an offer is generated for 30% off offers only to VIPs; etc.
  • the prespecified conditions and/or prespecified rules comprise one or more of the following: a diner satisfies a condition that had been set up by the customer of a restaurant (e.g., customer system user) or by a restaurant user (e.g., a restaurant owner) to provide incentive or triggers to finding offers, a diner is within x feet or y miles of the restaurant, a diner is a recurring diner, a diner has flagged explicitly a restaurant as a restaurant of interest, a diner has not dined at a restaurant in over x days, a diner has not used the reservation service in over x days, or any other appropriate condition and/or rule.
  • prespecified conditions and/or the prespecified rules are determined during a setup time by a restauranteur; the system generates, modifies, or discontinues offers in real time based on the rules and/or the prespecified conditions
  • the offers change in a graded way, a stepwise fashion, or a linear fashion over time.
  • a discount percentage changes from 10% to 20% to 30% as the date for the available inventory in a restaurant approaches. For example, 3 days before a $10 off discount is offered, 2 days before a $20 off discount is offered, and 1 day before a $30 discount is offered or vice versa.
  • an automated system is developed to make offers based at least in part on a rule set. For example, for inventory levels where table occupancy is less than 30% full, make offers until the table occupancy is 70% full. The offers gradually increase as the day of the reservations nears.
  • an automated offer start is based at least in part on a percentage full of a restaurant.
  • an automated offer end is based at least in part on a percentage full of a restaurant.
  • the set of rules indicates a series of steps of different offers: 5%, 10%, 15%, and 20% reduction.
  • the set of rules indicates a series of different types of offers: discount on beverage, discount on entrée, free entrée, etc.
  • an automated offer is based at least in part on preferred seating (e.g., a patio location, a view location, etc.).
  • Figure 3B is a flow diagram illustrating an embodiment of a flow for offer based restaurant reservations.
  • all availability of tables in restaurants is generated using availability rules.
  • availability rules For examples, 1) slot availability - dates, times and sizes are prespecified by the restaurant and are not directly related to actual tables; 2) allotment/allocation availability - the restaurant specifies a maximum number of diners or reservations that can be sold for a shift; also not directly related to the tables, for example - sell 30 covers over the course of the dinner shift which is from 5pm to 10pm; and 3) inventory availability - the restaurant describes the floor plan of the restaurant, including all tables and the party size range of each table such as tables 1 to 5 can each seat 2 to 6 covers; the restaurant also defines any tables that can be combined, and optionally specifies a fill order preference (example: seat diners at tables 10-12 first because they are at the window); the system automatically generates and shows all possible dates, times and party sizes based on these rules, and automatically updates the availability when reservations are made.
  • availability rules comprise one or more of the following: a rule that determines availability based at least in part on information from a restaurant indicating exact, approximate, or estimated table availability, a rule determining availability according to using all possible combinations and permutations of tables given possible arrangements and rearrangements of tables in a given restaurant, a rule determining availability according to using an allotment of tables, covers, or parties available for a shift or a rate of arrival, a rule determining availability according to using a slot model that indicates the party sizes and exact times that are available, a rule determining availability according to using inventory where the restaurant indicates exact tables and times that are available, or any other appropriate rule(s).
  • the availability of tables is filtered to determine pertinent table availability. For example, a database of available restaurant tables is searched for tables that satisfy availability criteria (e.g., in an area, with a type of cuisine, in a time range, in a date range, for a party size, etc.). For example, the availability of tables is filtered to optimize resources or to optimize revenue. In some embodiments, restaurant optimizes the use of section 2 as section 1 is temporarily closed.
  • all offers are generated using offer rules. For example: generate 2 for 1 drinks any Sunday through Thursday when the restaurant is going to be below 70% capacity, and stop generating these offers when the restaurant reaches 90%. Another example: the restaurant has sold out a room to a large party for a 4 hour period; the rule would be to generate a 30% off entire bill offer for the remaining 4 hours in the 8 hour shift to maximize the use of the kitchen/wait staff resources.
  • offer rules comprise one or more of the following: a rule that determines offer availability based at least in part on a date, a time, and a party size, a rule that determines real time offer availability of an offer so that offers cannot be oversubscribed as once the offer(s) are no longer desired, the offers are not made available, a rule that determines offer availability based at least in part on a campaign limit (e.g., a fixed cap that spans the duration of a campaign, which once depleted for the campaign then the offer is no longer available), on a daily limit (e.g., a fixed cap per day), on a slot based limit (e.g., a limit for a particular time), a rule that determines availability based at least in part on availability limits used in combination with each other, a rule that determines availability based at least in part on blackout dates or times or a blackout rule when offers are not available (e.g., peak times, holidays, theater times, etc.), a rule that determines
  • offers are filtered to determine pertinent offers. For example, offers are filtered to identify offers meeting search criteria (e.g., offers are within a desired time range, date range, cuisine type, location, neighborhood, discount range, discount type, party size, etc.). For example, a number of offers are determined in order to meet resource optimization goals (e.g., use of a room, use of wait staff, use of kitchen, use of food inventory, etc.).
  • search criteria e.g., offers are within a desired time range, date range, cuisine type, location, neighborhood, discount range, discount type, party size, etc.
  • resource optimization goals e.g., use of a room, use of wait staff, use of kitchen, use of food inventory, etc.
  • an overlapping subset is determined between pertinent table availability and pertinent offers.
  • yield management or revenue optimization rules are applied. For example, offers are made by applying rules that are designed to improve a restaurant's business. For example: the restaurant has sold out a room to a large party for a 4 hour period; the rule would be to generate a 30% off entire bill offer for the remaining 4 hours in the 8 hour shift to maximize the use of the kitchen/wait staff resources. Another example: generate free corkage offers only to restaurant VIPs who are known to be big spenders (based on their reservation & spending history). Another example: the restaurateur bought a large amount of beef; offer a 30% discount on all beef dishes.
  • offers are dynamically enabled or disabled based on a restaurant's business metrics (e.g., the current covers, reservations, and revenue accrued for the day, week, month, year vs. set thresholds or a deviation in the covers, reservations, and revenue vs. historical trends).
  • a short fall in one of these metrics trigger: (a) the enabling of an offer, (b) an increase in the number of offers being offered, (c) a change in the discount being offered, (d) an adjustment to the price of the offer.
  • the optimization rules feedback into the offers engine.
  • offer is removed based on a rule set (e.g., if offer is not sold within x days of a start offer date, offer is automatically removed).
  • FIG. 4 is a diagram illustrating an embodiment of a user interface display.
  • a user can input fields regarding a location, a type of cuisine or a specific restaurant, a date, a time, and a party size. The user can then also indicate to find a table.
  • a tab display shows meals for x% off. In some embodiments, the tab display is populated after the indication is selected. The tab display shows 20%-30% off meals at top city restaurants, which can be reserved at for $10. The discount can be applied discreetly to your bill. For example, Restaurant Italiano, located in Russian Hill and an Italian style restaurant, had 55 reviews and 4 stars for a rating, where 30% off can be received as a discount. Similarly, a $25 and 20% discounts can be received for Maharasha, Bert's Steak House, and Calamari restaurants. The discounts or promotions can be selected by pushing button 402.
  • Figure 5 is a diagram illustrating an embodiment of a user interface display.
  • a user can input fields regarding a date, a time, and a party size. For example, a user requests to locate a table for 6/25/2011 at 9:30 PM for 2 people. The user can then also indicate to find a table.
  • the display shows 4 restaurants offering 30%-off times around 9:30. Calamari restaurant has 30%-off offers available at 8:00, 9:15, and 9:30. Farrole restaurant has 30%-off offers available at 8:30 and 10:30.
  • Pui Pok has 30%-off offers available at 8:45, 9:30, and 9:45.
  • Bing's Steak House has 30%-off offers available at 8:30, 9:00, and 9:15.

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